Cerebral blood volume (CBV) is the fraction of each imaged voxel comprising the intravascular space and therefore the volume of the blood vessels within a volume of brain tissue. It is measured as milliliters of blood per 100 g of tissue; since brain tissue approximates water in density (1 g/mL), this can also be expressed as a percent value.

Typical values for the brain are between 3 % and 5 %. Only a small fraction of CBV is arterial, most of it being divided between capillaries and veins, and its changes are related to autoregulatory vasodilatation of the capillaries and/or veins [6, 7, 38]. CBV is a potentially sensitive indicator of vascular endothelial response to changes in local CBF and tissue metabolism.

Relative CBV can be measured as the area under the curve of the voxel concentration versus time. Absolute CBV can be determined by dividing the area under the curve by a “reference” voxel known to contain 100 % blood, such as the superior sagittal sinus [7, 39].

Unfortunately several technical factors can impair the accuracy of this relatively simple measurement [1, 3, 41]. The first is that dynamic susceptibility contrast arises from spin diffusion in the space surrounding the blood vessels, making it difficult to determine a reference 100 % blood-filled voxel [41]. Secondly, the effects of contrast recirculation must be eliminated or minimized. Thirdly, CBV measurement can be erroneous if the gadolinium chelate leaks through a disrupted BBB. This is of particular concern when imaging tumors, since several tumors cause BBB disruption, yielding a bright signal on post-contrast T1 images [7, 42] (Fig. 9.7).

DSC perfusion MRI model assumes that the contrast agent remains confined in the intravascular space throughout image acquisition. If the BBB is leaky, the DSC data can be compromised as the relative shortening of the T1 of water, due to parenchymal contrast extravasation, counteracts or mutes the T2* effect and the relative signal intensity decreases [8].

Comparative studies have shown that a “preload” of gadolinium chelate, given before the actual bolus dose for DSC data acquisition, can reduce the deleterious impact of a BBB breakdown on the perfusion data [8]. The preload reduces the T1 leakage effects by saturating the leaky tissues with contrast agent, thereby dampening the T1-shortening effects of the main bolus during DCS data acquisition [8].

New molecules, such as superparamagnetic iron-oxide particles, dendritic compounds saturated with gadolinium atoms, or reversible protein-binding gadolinium-based agents, have a longer half-life in blood [7, 43–45]. These so-called “blood pool” agents offer higher SNR for measuring CBV using steady-state susceptibility contrast that circumvents many of the problems outlined earlier.

Cerebral blood flow (CBF) is the amount of arterial blood delivered to brain tissue per unit of time. It is most commonly measured as milliliters of blood per 100 g of tissue per minute. Characteristic values for gray matter are 60 ml/min/100 g and 15 ml/min/100 g for white matter. CBF control is via control of the diameter of the feeding arterioles.

CBF is the primary rate constant controlling the supply of nutrients and removal of waste products from the brain. Below a threshold level, duration multiplied by the absolute CBF level can predict tissue infarction [7].

While CBV measurement with intravascular tracers is relatively straightforward, the measurement of CBF is more challenging [7, 46], as it requires delicate deconvolution methods.

In particular, one method tries to deconvolve the effects of a bolus of finite width (as estimated from the arterial signal near a large feeding vessel, such as the anterior communicating artery). This arterial input function is taken to represent the profile of the bolus at its point of entry into each individual voxel based on the assumption that every voxel has the same arterial input function. This is clearly an oversimplification, and uncertainties about regional changes in the profile and timing of the bolus can cause significant errors in CBF evaluation [7, 47].

Good absolute CBF correlation with H₂ ¹⁵O PET has been shown in anesthetized healthy pigs [48], and an excellent relative CBF correlation has been reported in an experimental model of ischemia [49]. However, in diseased brain, where collateral pathways are more common, intravascular tracer CBF measurements that do not account for variations in bolus delay (caused by arterial stenosis and occlusions) and dispersion are inaccurate [7, 46, 50].

Mean transit time (MTT) is the average amount of time the blood spends in the capillary bed. A capillary bed is made up of numerous capillaries of different lengths, so the time spent in the tissue has a distribution. The unit of measurement is seconds. Typical values for the normal brain are in the range of 3–5 s, whereas in acute cerebral infarction, MTT is increased.

According to the central volume principle, it is important to note close relationship between the CBF, CBV, and MTT. This relationship is:

This relationship can easily be explained with the simple case of flow through a single capillary. Flow is defined as the motion of a volume of fluid over time and velocity as distance over time. If the distance is measured along the capillary vessel, and the velocity is the speed of the fluid in this vessel, this relationship can be multiplied by the cross-sectional area of the capillary. This converts the distance to a volume and the velocity to a flow.

Time to peak (TTP) is the time the blood takes to reach the maximum intensity, from the start of the bolus injection to the peak concentration of contrast agent; it is defined as the time point of maximum intensity loss after the passage of the contrast agent. It is calculated in seconds and reveals any delays in transit time, enabling quick quantification of perfusion deficits. Ischemic areas are characterized by a delay of tracer arrival and a TTP increase [6, 51, 52]. A study of patients with acute cerebral ischemia postulated that TTP values of 0–3.5 s are related to normal perfusion; values ranging from 3.5 to 7 s may indicate a perfusion disorder, whereas values above 7 s indicate a high risk of ischemic tissue injury and watershed infarcts in border zones [51].

Despite these difficulties in determining the absolute values of volume and flow, relative values of CBV and CBF are extremely useful in clinical practice, especially in stroke evaluation.

It is generally accepted that tissue with abnormalities on both perfusion and diffusion-weighted imaging has already undergone irreversible ischemia.

However, tissue with perfusion abnormalities but normal diffusion is thought to be consistent with reversible ischemia. This area of diffusion-perfusion mismatch, also called ischemic penumbra, is a region of decreased perfusion that is potentially reversible because it is above the critical level for the maintenance of the Na⁺ K⁺-ATPase pump [1]. Exact evaluation of the tissue that can be rescued is critical in assessing the risk-benefit ratio of possible therapies, since patients with no mismatch are considered unlikely to benefit from thrombolytic therapy.

Time-dependent perfusion thresholds were first established using H₂ ¹⁵O PET to distinguish underperfused tissue evolving toward infarction ( < 12 ml/min per 100 g) from penumbral flow with functionally compromised but viable tissue (12–20 ml/min per 100 g) [1–3].

The exact role of each hemodynamic parameter (CBV, CBF, MTT, and TTP) in the correct evaluation of brain perfusion changes, and which of them is more representative of salvageable tissue and predictive of final infarct size is still debated.

It appears that MTT maps generally show the largest area of abnormality and often overestimate final infarct size, while CBV maps tend to underestimate this size.

In one study comparing CBV maps, CBF maps, and MTT maps with change in size from initial infarct size to final infarct size, a mismatch between initial CBF maps and a diffusion abnormality predicted more often the growth of infarct than did a mismatch between initial CBV maps and diffusion abnormality [41]. In that study, however, CBV maps best correlated with change in infarct size from initial to follow-up imaging [41].

A recent study comparing TTP maps and thresholds in patients with acute ischemia using CBF data acquired with PET, assumed as the gold standard, indicated that TTP is a useful estimate of ischemic injury but entails considerable methodological limitations that have to be considered in routine use of MRI for stroke [52].

Relative TTP maps are only indirect surrogates of CBF and do not represent the best application of MRI perfusion imaging. They are used in clinical routine because they are simple; they clearly delineate hemodynamic alterations, yield satisfactory results compared with quantitative methods, and do not rely on deconvolution algorithms, calibration with PET data, or selection of adequate input functions [52].

However, TTP maps may be inappropriate, since simple visual analysis is more prone to errors related to individual subjectivity [52].

A quantitative observer-independent measure such as TTP thresholds has proved to be more indicative of underperfusion stated by PET for a CBF value of < 20 ml/min per 100 g [52].

The best estimate of penumbral flow was found for a TTP delay of > 4 s (sensitivity 84 %, specificity 77 %); it best identifies the volume of penumbra and should be used to define the mismatch volume. The volume of a TTP delay of > 4 s was correlated with clinical deficit, whereas the volume of a TTP delay between > 5 and > 8 s was strongly associated with infarct growth [52].

The lack of a complete match between PET and TTP thresholds is partly explained by method-specific properties. TTP only yields a relative estimation of CBF as it indicates the time point of maximum signal intensity loss during the passage of the tracer within several seconds. H₂ ¹⁵O PET assesses the “true” CBF as the concentration of a partly diffusible tracer integrated over a scanning time of several minutes. TTP data are therefore more prone to movement artifacts, collateral flow, or individual hemodynamic properties [1, 52].

However, even the TTP threshold with the best sensitivity and specificity seems to include a large portion of tissue with only modest hemodynamic damage; particularly in small ischemia, TTP tends to overestimate the true extent of the “tissue at risk” [52].